Split ERT 3 parameters into separate records

ert3/engine/_record.py

@@ -47,4 +47,5 @@ def sample_record(
         record_name=record_name,
         ensemble_record=ensrecord,
         experiment_name=experiment_name,
+        is_parameter=True,
     )

ert3/engine/_run.py

@@ -9,15 +9,21 @@ def _prepare_experiment(
     experiment_name: str,
     ensemble: ert3.config.EnsembleConfig,
     ensemble_size: int,
+    parameters_config: ert3.config.ParametersConfig,
 ) -> None:
     if ert3.workspace.experiment_has_run(workspace_root, experiment_name):
         raise ValueError(f"Experiment {experiment_name} have been carried out.")
 
-    parameter_names = [elem.record for elem in ensemble.input]
+    parameter_names = {elem.record for elem in ensemble.input}
+    parameters = {
+        param.name: list(param.variables)
+        for param in parameters_config
+        if param.name in parameter_names
+    }
     ert3.storage.init_experiment(
         workspace=workspace_root,
         experiment_name=experiment_name,
-        parameters=parameter_names,
+        parameters=parameters,
         ensemble_size=ensemble_size,
     )
 
@@ -64,7 +70,9 @@ def _prepare_evaluation(
     # This reassures mypy that the ensemble size is defined
     assert ensemble.size is not None
 
-    _prepare_experiment(workspace_root, experiment_name, ensemble, ensemble.size)
+    _prepare_experiment(
+        workspace_root, experiment_name, ensemble, ensemble.size, parameters_config
+    )
 
     for input_record in ensemble.input:
         record_name = input_record.record
@@ -111,7 +119,9 @@ def _prepare_sensitivity(
     )
     input_records = ert3.algorithms.one_at_the_time(parameter_distributions)
 
-    _prepare_experiment(workspace_root, experiment_name, ensemble, len(input_records))
+    _prepare_experiment(
+        workspace_root, experiment_name, ensemble, len(input_records), parameters_config
+    )
 
     parameters: Dict[str, List[ert3.data.Record]] = {
         param.record: [] for param in ensemble.input